Method and apparatus that utilises fluorescence to determine plant or botanical origin characteristics of honey

ABSTRACT

Methods and apparatus are described for the measurement of honey plant origin characteristics via fluorescence.

RELATED APPLICATIONS

This application is a 371 national stage application ofPCT/NZ2011/000248, filed Nov. 29, 2011. This application claims priorityfrom NZ589582 dated 29 Nov. 2010, the contents of which are incorporatedherein by reference.

TECHNICAL FIELD

The application relates to a method and apparatus for honey measurement.More specifically, the application relates to a method and apparatusthat utilises fluorescence to determine plant or botanical origincharacteristics of honey.

BACKGROUND ART

Honey analysis can be important to determine characteristics of honeysuch as honey plant or botanical origin, honey contamination and honeythat has been processed in ways that influence the purported activity.Determining honey plant origin is of particular interest for qualitycontrol purposes e.g. for determining medical grade high activity honeyfrom those with less medical activity. Reassurance of origin is alsoimportant as the value of some honeys e.g. manuka honey, may be markedlyhigher than that of other honey types.

Measuring plant origin characteristics can be difficult, particularly asthere are many different measures that can be analysed to measure honeyquality. In addition, many of the existing tests can take 24 hours ormore before the results are obtained. This delay in receiving resultscan impact on processing by delaying blending operations and delayingquality control inspections, both of which may impact on processingcosts.

It is known that honey will fluoresce. This is understood to be due tothe presence of aromatic compounds in the honey (mainly phenoliccompounds) that may be excited by light and that then emit light inresponse to the excitation. One prior art patent publication, WO2010/027286A1 uses the property of fluorescence to analyse honey butonly measures the result using two parameters meaning that much of thevaluable characteristic finger print fluorescence of a honey is notvisible. This therefore results in incorrect readings or the potentialof not obtaining a valid result.

It should be appreciated from the above that it would be useful to havea method and apparatus for at least measuring plant origincharacteristics of honey. In particular, a faster method than that ofpresent methods would be useful.

It is acknowledged that the term ‘comprise’ and grammatical variationsthereof may, under varying jurisdictions, be attributed with either anexclusive or an inclusive meaning. For the purpose of thisspecification, and unless otherwise noted, the term ‘comprise’ shallhave an inclusive meaning.

Further aspects and advantages of the presently described devices andmethods will become apparent from the ensuing description that is givenby way of example only.

SUMMARY

The application broadly relates to a method and device that usesfluorescence to measure the presence and concentration of keyconstituents of honey as well as determining the botanical origin of ahoney.

In some embodiments there is provided a method for determining theconcentration values of key constituent chemicals of honey, includingthe steps of:

-   -   (a) estimating the botanical origin of at least one standard        honey sample, by:        -   (i) obtaining key constituent chemical concentrations; and        -   (ii) assigning the botanical origin as a numerical value on            the basis of abundance of chemical compounds characteristic            of certain botanical groups;    -   (b) generating the fluorescence signature of standard honey        samples, by:        -   (i) exciting a diluted honey sample solution with light of            wavelengths over the range 200-700 nm at increasing            increments; and        -   (ii) measuring the intensity of the fluorescent light            emitted from the excited solution over the range 280-650 nm            at increasing increments; and        -   (iii) measuring the wavelength of the fluorescent light            emitted from the excited solution over the range 280-650 nm            at increasing increments; and        -   (iv) combining excitation and emitted light as 2-dimensional            excitation-emission matrix (EEM);    -   (c) constructing a validated predictive mathematical model from        standard honey data, by:        -   (i) using the botanical origin value determined in step (a)            as the first matrix in a multivariate analysis:        -   (ii) using the fluorescence EEM data determined in step (b)            as the second matrix in a multivariate analysis;        -   (iii) generating a mathematical model using these two            matrices; and,        -   (iv) establishing a statistical confidence of predictive            power of mathematical model with leave-one-out validation            process;        -   (d) generate the fluorescence EEM signature of an unknown            honey sample or samples, as outlined in step (b); and        -   (e) using the unknown honey fluorescence EEM data from            step (d) with the validated mathematical model of step (c)            to predict and assign concentration values of key            constituent chemicals of honey with defined statistical            confidence.

In some embodiments there is provided a method for determining thebotanical origin of honey including the steps of:

-   -   (a) estimating the botanical origin of standard honey samples,        by:        -   (i) obtaining key constituent chemical concentrations; and        -   (ii) assigning the botanical origin as a numerical value on            the basis of abundance of chemical compounds characteristic            of certain botanical groups;    -   (b) generating the fluorescence signature of standard honey        samples, by:        -   (i) exciting a diluted honey sample solution with light of            wavelengths over the range 200-700 nm at increasing            increments; and        -   (ii) measuring the intensity of the fluorescent light            emitted from the excited solution over the range 280-650 nm            at increasing increments; and        -   (iii) measuring the wavelength of the fluorescent light            emitted from the excited solution over the range 280-650 nm            at increasing increments; and        -   (iv) combining excitation and emitted light as 2-dimensional            excitation-emission matrix (EEM);    -   (c) constructing a validated predictive mathematical model from        standard honey data, by:        -   (i) using the botanical origin value determined in step (a)            as the first matrix in a multivariate analysis;        -   (ii) using the fluorescence EEM data determined in step (b)            as the second matrix in a multivariate analysis;        -   (iii) generating a mathematical model using these two            matrices; and,        -   (iv) establishing a statistical confidence of predictive            power of mathematical model with leave-one-out validation            process;    -   (d) generate the fluorescence EEM signature of an unknown honey        sample or samples, as outlined in step (b); and    -   (e) using the unknown honey fluorescence EEM data from step (d)        with the validated mathematical model of step (c) to predict and        assign numerical value of botanical origin of honey with defined        statistical confidence.

In some embodiments there is provided a device for identifying honeybotanical origin and/or chemical constituents that includes a samplereceiving area into which a honey sample is inserted and the devicesubsequently identifies the honey botanical origin and chemicalconstituents via the method as claimed in any one of the above claims.

The methods and device provide a fast and simple way to quicklydetermine at least qualitatively the botanical origin of a honey sample.This is useful for a variety of reasons including for quality controland to ensure correct labelling of honey as to the source. The methodsand device also measure the full characteristic fingerprint of a honeyincluding peaks at varying excitation and emission wavelengths that aremissed if only one wavelength (excitation or emission) is measured.

DESCRIPTION OF THE FIGURES

Further aspects of the application will become apparent from thefollowing description that is given by way of example only and withreference to the accompanying drawings in which:

FIG. 1 shows a graph comparison of trained model (‘actual’) data withpredicted data for the independent validation honey samples;

FIG. 2 shows a graph comparison of three-wavelength data with full-scandata for the validated honey samples;

FIG. 3 shows a graph comparison of measured and predicted levels ofmethyl syringate concentrations, from the EEM data using the NPLS model;

FIG. 4 shows a graph comparison of measured and predicted levels of2-methoxybenzoic acid concentrations, from the EEM data using the NPLSmodel;

FIG. 5 shows a graph comparison of measured and predicted levels ofphenyllactic acid concentrations, from the EEM data using the NPLSmodel;

FIG. 6 shows a graph comparison of measured and predicted levels ofdihydroxy acetone (DHA) concentrations, from the EEM data using the NPLSmodel;

FIG. 7 shows a graph comparison of measured and predicted levels of4-methoxy phenyllactic acid concentrations, from the EEM data using theNPLS model;

FIG. 8 shows a graph comparison comparing the measured and predictedlevels of methylglyoxal (MGO) concentrations, from the EEM data usingthe NPLS model;

FIG. 9 shows a graph comparison of measured and predicted levels of theratio between dihydroxy acetone and methyl glyoxal concentrations, fromthe EEM data using the NPLS model;

FIG. 10 shows a graph comparison of measured and predicted levels of thesum of dihydroxy acetone (DHA) and methylglyoxal (MGO) concentrationsfrom the EEM data using the NPLS model;

FIG. 11 shows an example of the changing ratio of the two dye peaks(265,615 and 590,620) as the concentration of honey in a solutionchanges;

FIG. 12 shows an example of the changing ratio of two honey peaks (red)and the inverse of two dye peaks (blue) as the concentration of honey ina solution changes;

FIG. 13 illustrates a comparison scan between diluted kanuka honeysexposed to light (left) and stored in the dark (right);

FIG. 14 illustrates a comparison scan between concentrated manuka honeysexposed to light (left) and stored in the dark (right);

FIG. 15 illustrates a comparison scan between concentrated kanuka honeysexposed to light (left) and stored in the dark (right);

FIG. 16 illustrates a comparison scan between diluted manuka honeysexposed to light (left) and stored in the dark (right);

FIG. 17 illustrates fluorescence spectra of wild nectar samples. A:NF,B:BS, C:WB, D:Rh1, E:Rh2;

FIG. 18 illustrates the fluorescence spectra from nectar samplescollected in a glasshouse. A: Leptospermum scoparium var. incanum, B:Leptospermum scoparium var. ‘West Coast South Island’ (un-named), C:Leptospermum scoparium var. incanum cultivar, D: Leptospermum scopariumvar. ‘triketone” cultivar (probably contains some var. incanumparentage), E: Leptospermum scoparium var. incanum cultivar, F:Leptospermum spectabile cultivar (Australian species), G: Leptospermumpolygalifolium (Australian Species), H: Leptospermum continentale(Australian species); and

FIG. 19 illustrates hotelling T2 vs. Q residuals plot plotting the honeysamples as the calibration set and the nectar samples as the test set.

DETAILED DESCRIPTION

As noted above, the application broadly relates to a method and devicethat uses fluorescence to measure the presence and concentration of keyconstituents of honey as well as determining the botanical origin of ahoney.

For the purposes of this specification, the term ‘fluorescence’ andgrammatical variations thereof refers to the emission of light by honeythat has absorbed light or any electromagnetic radiation of a differentwavelength.

The term ‘honey’ refers to naturally produced honey containing at leasta mix of glucose, fructose, water and glucose oxidase enzyme as well asplant derived compounds including aromatic phenolic compounds.

The term ‘honey type’ refers to a honey from a particular plant originor a blend of plant origins.

The term ‘plant origin’ and ‘botanical origin’ are used interchangeablyand refer to the plant nectar that the honey is derived from asevidenced by the specific compound(s) present in the honey that arederived from the plant.

The term ‘intensity’ refers to how intense the emission of energy isform the honey sample. A high intensity refers to release of high levelsof energy relative to general levels. More specifically, the term highintensity refers to the energy level being greater than 30%, 40%, 50%,60%, 70%, 80%, 90%, or 100% higher than a baseline energy level such asthat observed for a low phenolic concentration honey, one example beingclover honey.

The term ‘peak’ refers to a maximum emission intensity expressed as arange in the case of a wide range of wavelengths or a point in the caseof a specific wavelength or wavelengths.

The term ‘excitation’ and grammatical variations thereof refers to theuse of electromagnetic radiation elevate the energy level of themolecules and atoms in a honey sample from a ground state.

The term ‘emission’ and grammatical variations thereof refers to therelative intensity of electromagnetic radiation of any wavelengthemitted by the honey compound's molecules when they return to a groundstate after being moved to an excited state.

The term ‘purity’ refers to the honey being a monofloral honey.

The term ‘monofloral honey’ refers to the honey being predominantlyderived from one plant species.

In some embodiments there is provided a method for determining theconcentration values of key constituent chemicals of honey, includingthe steps of:

-   -   (a) estimating the botanical origin of at least one standard        honey sample, by:        -   (i) obtaining key constituent chemical concentrations; and        -   (ii) assigning the botanical origin as a numerical value on            the basis of abundance of chemical compounds characteristic            of certain botanical groups;    -   (b) generating the fluorescence signature of standard honey        samples, by:        -   (i) exciting a diluted honey sample solution with light of            wavelengths over the range 200-700 nm at increasing            increments; and        -   (ii) measuring the intensity of the fluorescent light            emitted from the excited solution over the range 280-650 nm            at increasing increments; and        -   (iii) measuring the wavelength of the fluorescent light            emitted from the excited solution over the range 280-650 nm            at increasing increments; and        -   (iv) combining excitation and emitted light as 2-dimensional            excitation-emission matrix (EEM);    -   (c) constructing a validated predictive mathematical model from        standard honey data, by:        -   (i) using the botanical origin value determined in step (a)            as the first matrix in a multivariate analysis;        -   (ii) using the fluorescence EEM data determined in step (b)            as the second matrix in a multivanate analysis,        -   (iii) generating a mathematical model using these two            matrices; and,        -   (iv) establishing a statistical confidence of predictive            power of mathematical model with leave-one-out validation            process;    -   (d) generate the fluorescence EEM signature of an unknown honey        sample or samples, as outlined in step (b); and    -   (e) using the unknown honey fluorescence EEM data from step (d)        with the validated mathematical model of step (c) to predict and        assign concentration values of key constituent chemicals of        honey with defined statistical confidence.

In some embodiments there is provided a method for determining thebotanical origin of honey including the steps of:

-   -   (a) estimating the botanical origin of standard honey samples,        by:        -   (i) obtaining key constituent chemical concentrations; and        -   (ii) assigning the botanical origin as a numerical value on            the basis of abundance of chemical compounds characteristic            of certain botanical groups;    -   (b) generating the fluorescence signature of standard honey        samples, by:        -   (i) exciting a diluted honey sample solution with light of            wavelengths over the range 200-700 nm at increasing            increments; and        -   (ii) measuring the intensity of the fluorescent light            emitted from the excited solution over the range 280-650 nm            at increasing increments; and        -   (iii) measuring the wavelength of the fluorescent light            emitted from the excited solution over the range 280-650 nm            at increasing increments; and        -   (iv) combining excitation and emitted light as 2-dimensional            excitation-emission matrix (EEM);    -   (c) constructing a validated predictive mathematical model from        standard honey data, by:        -   (i) using the botanical origin value determined in step (a)            as the first matrix in a multivanate analysis;        -   (ii) using the fluorescence EEM data determined in step (b)            as the second matrix in a multivariate analysis;        -   (iii) generating a mathematical model using these two            matrices; and,        -   (iv) establishing a statistical confidence of predictive            power of mathematical model with leave-one-out validation            process;    -   (d) generate the fluorescence EEM signature of an unknown honey        sample or samples, as outlined in step (b); and    -   (e) using the unknown honey fluorescence EEM data from step (d)        with the validated mathematical model of step (c) to predict and        assign numerical value of botanical origin of honey with defined        statistical confidence.

In the above embodiments, the numerical value of botanical origin may beexpressed as a percentage manuka honey, percentage kanuka honey,percentage other specific floral origin honey, percentage other originhoney as a sum, and combinations thereof.

The constituent chemicals in a honey and/or the honey floral origin maybe determined instead by analysis of the nectar from which the honey isderived.

In the above methods, the fluorescence signature may be generated usingexcitation wavelengths in the range 200-700 nm. Alternatively, thefluorescence signature may be generated using the key excitationwavelengths, 230 nm, 265 nm, and 335 nm. As may be appreciated, use ofthree or four excitation wavelengths instead of full EEM scanning ispotentially simpler and cheaper to complete and may be preferable wherea portable instrument for use in the field is to be produced.

For the purposes of this specification, the term ‘standard honey sample’or grammatical variations thereof may be those honeys for which priorknowledge of honey age and/or one or more chemical constituents exists.Traditional methods of analysis of phenolic content may be by use ofseparation via high performance liquid chromatography (HPLC) followed byUV or fluorescence detection and comparison with a known standard.Alternatively, traditional methods of analysis may include HPLC followedby mass spectroscopy (HPLC-MS) to separate and identify compounds. Asmay be appreciated, the above traditional methods of analysis requirespecialised and expensive equipment (and the equipment is typically notportable). Specialised knowledge to operate the equipment is alsorequired and specialist software tools are needed to analyse theresults. In addition, some compounds require different processing priorto the above HPLC analyses further exacerbating the complexity of theanalysis process.

The key constituent chemicals may include compounds selected from:methyl syringate, 2-methoxybenzoic acid, phenyllactic acid,4-methoxyphenyllactic acid, dihydroxyacetone (DHA), methylglyoxal (MGO),and combinations thereof. Compounds listed are known to be markers ofbotanical origin for at least some common types of honey. Absence of orhigher concentrations of these compounds signify particular honeyorigins. In addition MGO is known to be directly attributable to the UMFor antibacterial activity of a honey hence is a common marker of honeyvalue and widely used in honey labelling. DHA is a precursor compound toMGO and over time converts to MGO hence is also a key marker compound inhoney.

As may be appreciated, the correlation between the phenolic profiles andlevels of the antimicrobial (UMF®) molecule MGO and its precursor DHAwas quite unexpected. Phenolic compounds fluoresce due to the presenceof or one or more aromatic rings in the chemical structure. Thecompounds DHA and MGO are comparatively simple compounds that do notdirectly fluoresce. The applicants found that accurate predictions ofDHA and MGO concentrations could be measured by analysis of the phenolicconcentrations and insertion into the model. It appears that one or moremarker phenolic compounds (e.g. 2-methoxybenzoic acid) in honey directlycorrelates to the presence of DHA and/or MGO. The applicants haveidentified that the DHA content is directly proportional to the phenolicconcentrations or at least selected manuka honey characteristic levels.This relationship also unexpectedly stays proportional over time as boththe phenolic concentration and the DHA concentration decay at the sameexponential (Arrhenius) rate. The same rate of decay is surprising asother compounds such as MGO does not decay in the same manner. Knowncorrelations from the art describe the correlation between DHA and MGOconcentration hence, if the DHA level is known, the MGO and/or UMFactivity may also be calculated.

The ability to measure DHA and MGO contents is particularly useful asMGO levels (responsible for the UMF® activity of some honeys) areattributable to the antimicrobial activity of so called ‘active’ ormedical honeys. High UMF/MGO concentration honeys typically attractgreater monetary value hence there is some need to quickly measure anddetermine that the label or stated MGO/UMF level is in fact correct.Adulterating the MGO/UMF level is relatively simple to obtain a highervalue honey yet, adulteration may in fact leave behind undesirablecompounds—one example being heating of honey which leaves behindunwanted HMF compounds. Note also that DHA is a precursor compound toMGO and in time, DHA converts to MGO hence also knowing DHA levels addsto the overall analysis.

The leave-one-out validation process may involve re-creating the modelin the absence of selected standard honey data, and then enteringstandard honey fluorescence data as unknown honey to predict botanicalorigin and chemical constituent concentrations, and determiningstatistical variance from known values. In some embodiments themathematical model used may be a partial least squares (PLS) analysis.THE PLS may be an N-way PLS (NPLS) method.

As may be appreciated, the above methods may be completed for a widevariety of reasons. In some embodiments, if the determined keyconstituent concentration and/or botanical origin of the unknown honeyor honeys do not agree with the labelling affixed to a honey, the honeymay be rejected and not processed. In alternative embodiments, themethods may be used to determine the monetary value of the honey basedon the botanical origin purity and/or chemical constituents. In otherembodiments, the methods may be used to characterise honeys on the basisof chemical constituents that may have biological activity including,but not limited to, antimicrobial, antioxidant, immunomodulatory orneuroendocrine activities. In other embodiments, the methods may be usedto characterise honeys for use as standard mixtures for calibrationpurposes for the fluorescence profile comparison of other, unrelatedmixtures. In other embodiments, the methods may be used to characterisehoneys on the basis of seasonal or climatic variation, flowering time,rainfall or wind patterns or alternative environmental concerns whichalter the availability and composition of the nectar from which thehoney is derived. In further embodiments, the former characterisationmight be used to generate calibration standards or biomarkers for thecomparison of these environmental factors. In other embodiments, themethods may be used to characterise honeys on the basis of bee or hivestatus which may impact on either collection of nectar from which thehoney is derived, or on the condensation process which occurs within thehive, from which the honey is derived. In further embodiments, theformer characterisation might be used to generate calibration standardsor biomarkers for the comparison of these bee or hive factors.

The sample or samples may be initially diluted to a 0.2 to 5% w/vsolution using water. The sample or samples may be initially diluted toan approximately 0.5%, 0.75%, 1.0%, 1.25%. 1.5%. 1.75%. 2.0%. 2.25%,2.5%, 2.75%, 3.0%, 3.25%, 3.5%, 3.75%, 4.0%, 4.25%, 4.5%, 4.75% w/vsolution using water. The dilution may be approximately 2% w/v. Theapplicants have determined that the level of dilution is important toobtaining an accurate result. If the concentration is too low or toohigh the accuracy of the method decreases dramatically. The termapproximately is used above and refers to the amount described varyingby 1%, 2%, 5%, 10%, 15% or 20% from that stated. The water may ideallybe sterilised and/or deionised.

The sample or samples may include a dye that provides a controlintensity and frequency of fluorescence. In some embodiments, the dyemay be Alexa Fluor™ dye 594 that emits at 625 nm although it should beappreciated that other dyes may also be used that fluoresce outside therange of that measured. Alexa Fluor dye is advantageous as it not onlyfluoresces outside the range analysed but also is sufficiently stable tonot photo bleach in light or deteriorate in heat at any appreciablerate. In some embodiments, the dye or dyes used are sufficiently lightstable so as to not photo bleach when stored in light in a diluted stateover a time period of 4 hours.

Alternatively, the dye may be replaced with quantum dots instead for useas a standard. In further embodiments both a dye or dyes and quantumdots may be used.

The applicants have found that the higher the peak intensity of themeasured sample, the greater the botanical origin purity or monofloralnature of the sample. This is particularly the case for Leptospermum andKunzea genus plant origin honeys although this may also be the case withother botanical origin honeys and the methods may be used to at leastaccurately distinguish the floral purity or concentration Leptospermumgenus, Kunzea genus and collectively other variety plant origin honeys.

For the purposes of this specification reference to Leptospermum genusplants includes manuka however, this should not be seen as limiting asother Leptospermum species have similar phenolic compounds and henceresults found for manuka species are also observed for other

Leptospermum Species.

For the purposes of this specification reference to Kunzea genus plantsincludes kanuka however, this should not be seen as limiting as otherKunzea species have similar phenolic compounds and hence results foundfor kanuka species are also observed for other Kunzea species.

To further illustrate the nature of the results obtained from analysis,a variety of characteristics excitation wavelengths and peak intensitiesare provided for varying honey floral origins. The figures providedshould be seen as trends and the actual figures may vary up to 10 to 20%from that illustrated. It should further be noted that the analysisdescribed in the above claims tends to place more weight on qualitativetrends as opposed to the quantitative figures below. As a result,specific peaks or frequencies described, whilst being important to theoverall results are used to provide trends in determining the overallresults.

Peak intensity above 30,000 may indicate the presence of eitherLeptospermum genus, Kunzea genus origin honey or both honeys.

Peak intensity at 270 nm and 340 nm excitation corresponding to 380 nm,440 nm and 490 nm emission may indicate the presence of Leptospermumgenus origin honey.

If a maximum intensity above 30,000 exists and there is no peakwavelength located at 230 nm excitation and 310 nm emission, the honeymay be Leptospermum genus honey.

If a maximum intensity between 10,000 and 30,000 exists and the highestor second highest peak is located at 270 nm excitation and 370-380 nmemission and there are two small peaks at 340 nm excitation and there isno peak located at 230 nm excitation and 310 nm emission, the honey maybe a Leptospermum genus honey blended with other honey.

If the peak intensity occurs at 230 nm, 280 nm and 270 nm excitationcorresponding to 310 nm and 380 nm emission, the honey may be Kunzeagenus origin honey.

If a maximum intensity above 30,000 exists and there is a peakwavelength located at 230 nm excitation and 310 nm emission, the honeyis of Kunzea genus origin.

In the case of Kunzea genus origin, a phenolic compound, the applicantshave identified 4-methoxyphenyl lactic acid, as the phenolic compoundthat fluoresces at 230 nm excitation and 310 nm emissions. This is acompound that is mainly found within kanuka origin honeys. It isanticipated that other plant origin specific compounds will eventuallybe identified corresponding to the various peaks and emissionfrequencies observed.

If a maximum intensity between 10,000 and 30,000 exists and the highestor second highest peak is located at 270 nm excitation and 370-380 nmemission and there are two small peaks at 340 nm excitation and a peaklocated at 230 nm excitation and 310 nm emission, the honey may be ablend of both Leptospermum genus and Kunzea genus honey.

If the maximum intensity is below 10,000 and there are two distinctpeaks where the trough is greater than half the peak height and thepeaks are above 5,000, the honey may be either Trifolium genus orWeinmannia silvicola species honey.

For the purposes of this specification reference to Trifolium genusplants includes clover however, this should not be seen as limiting asother Trifolium species have similar phenolic compounds and henceresults found for clover species are also observed for other Trifoliumspecies.

For the purposes of this specification reference to Weinmannia silvicolaspecies plants includes towei however, this should not be seen aslimiting as other Weinmannia silvicola species have similar phenoliccompounds and hence results found for towai species are also observedfor other Weinmannia silvicola species.

Peak intensity at approximately 230 nm, 280 nm and 260 nm excitationcorresponding to 310 nm, 360 nm and 490 nm emission may indicate thatthe sample may be Weinmannia silvicola species origin honey.

If a peak exists at 350 nm emission, the honey may be of Trifolium genusor Ixerba genus origin.

For the purposes of this specification reference to Ixerba genus plantsincludes tawai however, this should not be seen as limiting as otherIxerba genus plants have similar phenolic compounds and hence resultsfound for tawari species are also observed for other Ixerba genusplants.

A peak intensity at approximately 280 nm and 230 nm excitationcorresponding to 350 nm emission indicates that the sample is Ixerbagenus or Trifolium genus origin honey.

If the maximum intensity is below 10,000 and there are two distinctpeaks where the trough is greater than half the peak height and thepeaks are below 5,000, the honey may be of Ixerba genus origin.

A peak intensity at approximately 280 nm, 230 nm and 250 nm excitationcorresponding to 360 nm, 370 nm and 490 nm emission may indicateMetrosideros excelsa origin honey.

For the purposes of this specification reference to Metrosideros excelsaspecies plants includes pohutukawa however, this should not be seen aslimiting as other Metrosideros excelsa species plants have similarphenolic compounds and hence results found for pohutukawa species arealso observed for other Metrosideros excelsa species.

If the maximum intensity is below 10,000 and there are three distinctpeaks where the trough is greater than half the peak height withintensity above 2,000, the honey may be of Metrosideros excelsa origin.

If the maximum intensity is below 10,000 and there are more four or moredistinct peaks where the trough is greater than half the peak height,the honey may be selected from Metrosideros genus, Weinmannia genus orKnightea genus honey.

For the purposes of this specification reference to Metrosideros genusspecies plants includes rata however, this should not be seen aslimiting as other Metrosideros genus species plants have similarphenolic compounds and hence results found for rata species are alsoobserved for other Metrosideros genus species.

For the purposes of this specification, reference to Weinmannia genusspecies plants includes kamahi however, this should not be seen aslimiting as other Weinmannia genus species plants have similar phenoliccompounds and hence results found for kamahi species are also observedfor other Weinmannia genus species.

For the purposes of this specification, reference to Knightea genusspecies plants includes rewarewa however, this should not be seen aslimiting as other Knightea genus species plants have similar phenoliccompounds and hence results found for rewarewa species are also observedfor other Knightea genus species.

A peak intensity at approximately 270 nm, 260 nm, 230 nm and 260 nmexcitation corresponding to 370 nm, 490 nm, 380 nm and 450 nm emissionmay indicate Metrosideros genus origin honey.

If the maximum intensity is above 10,000 and there are more four or moredistinct peaks where the trough is greater than half the peak height,the honey may be Nothofagus genus or Knightea genus honey.

For the purposes of this specification, reference to Nothofagus genusspecies plants includes beech however, this should not be seen aslimiting as other Nothofagus genus species plants have similar phenoliccompounds and hence results found for beech species are also observedfor other Nothofagus genus species.

A peak intensity at approximately 270 nm and 230 nm excitationcorresponding to 370 nm and 380 nm emission may indicate Knightea genusorigin honey.

If peak intensity exists of approximately 10,000 at scan coordinates 270nm and 230 nm excitation corresponding to 370 nm and 380 nm emission,the honey may be of Knightea genus origin.

Peak intensity at approximately 290 nm and 230 nm excitationcorresponding to 390nm and 400 nm emission may indicate Nothofagus genusorigin honey.

Peak intensity at approximately 280 nm, 230 nm 240nm and 260 nmexcitation corresponding to 360 nm, 390 nm, 440 nm and 490 nm emissionmay indicate Weinmannia genus origin honey.

As should be appreciated, variation in the figures provided may occurwithout departing from the scope of the embodiments described herein. Asa general rule, the fluorescent wavelength may vary plus or minus 5 nm,10 nm, 15 nm, 20 nm, 25 nm, 30 nm, 35 nm or 40 nm from the stated figurehowever, the trends and standards described still hold true and may beused to at least qualitatively identify the various species origins ofthe honey sample(s).

Manipulations to honey may also be measured or inferred from the abovemethod. Manipulations to honey may be for a variety of reasons.Manipulations may include addition of DHA or MGO in order to manipulatethe MGO level. Alternatively, manipulations may include heating of thehoney and/or pH adjustment. Since these manipulations can artificiallyincrease the monetary value of a honey, knowing whether or notmanipulation has occurred may be of considerable importance.

In some embodiments there is provided a device for identifying honeybotanical origin and/or chemical constituents that includes a samplereceiving area into which a honey sample is inserted and the devicesubsequently identifies the honey botanical origin and chemicalconstituents via the methods substantially as hereinbefore described.

As should be appreciated from the above description, after analysis tooptimise the method settings and results, it was found to be possible todistinguish between honey samples from different botanical origin and todetermine the chemical concentration of selected constituents in thehoney. The analysis of a honey sample is based on its fluorescenceintensity, the location of peaks and optionally, the ratio of peakheights and the ratio of slopes. Excitation and fluorescence is possibledue in part to the aromatic nature of the phenolic compounds present inhoney. These aromatic compounds are derived from the plant species thatthe honey is produced from and the specific phenolic compounds varydepending on the plant species. In effect, the aromatic phenoliccompounds leave a chemical fingerprint in the honey showing which plantspecies the honey is derived from. Analysing honey for plant speciesorigin has been possible but typically tests are slow. The presentmethods and device provide means to rapidly obtain at least aqualitative determination of the honey origin. With refinement, themethods and device may also potentially be used to quantitativelydetermine the honey plant origin and composition. The methods and devicemay also be used in conjunction with more traditional tests, for examplewhere the first fluorescence scan is not totally clear as to the origin.

Further details are provided in the working examples below.

WORKING EXAMPLES

The application is now described with reference to examples illustratingembodiments of the methods and device.

Example 1

In this example, a model to predict honey compound concentration andbotanical origin was produced, validated and tested against unknownhoney samples.

Seventy-five honey samples were collected accompanied by somecomposition data and estimates of floral origin.

The honey samples were diluted to a 2% (w/v) solution in a 0.05%solution of fluorescent dye (Alexa Dye 594) in deionised water. Blendsof the honey solutions were made by mixing two diluted honeys togetherin even volumes.

The fluorescence of honey was measured using a Tecan XFLUOR4 SAFIRE II(Tecan Austria GmbH, Austria).

Black (100 μL well volume) 384-well plates were used for each scan.Black plates were used because white plates reflect fluorescent rays andtransparent plates transmit light from adjacent wells. These deep wellplates were used to minimise the effects of evaporative sample loss.

The measurement mode used was the fluorescence top 3D scan. Theintegration was 2000 μs with the flash mode set to high sensitivity. Thegain was set at 85 and the z position at 10,622 μm. The range ofexcitation wavelengths was between 230 and 400 nm, while the emissionscans ranged from 280 to 650 nm. Readings were taken at increasing 5 nmincrements in both the emission and excitation ranges.

When the excitation and emission wavelengths of honey samples overlappedthere was an apparent significant fluorescent output. This is notfluorescence from the samples, but an artefact of the photomultiplierdetecting the excitation light. This artefact has been subtracted fromthe analyses.

Data Pre-Processing

The raw data from the Tecan XFLUOR4 SAFIRE II are presented in anExcitation-Emission Matrix (EEM) for each sample. All samples that wereanalysed contain an internal standard to eliminate the effects ofevaporation. The internal standard used was Alexa Fluor 594 Dye, whichhas a fluorescent spectrum that does not overlap with the fluorescentspectrum of the honey. To remove evaporative losses that affect thedata, points were adjusted so that the data points are still in the sameratios to one another. The peak chosen as the baseline was located at265 nm excitation and 615 nm emission, which was set to 10,000 in allsamples. All of the points were changed accordingly by dividing eachdata point by the value of the set peak in the raw data, and multiplyingthe result by 10,000.

Once the data had been normalised, the fluorescent data of a blanksample was removed so that the background fluorescence due to thesolvent and the dye could not be seen. The blank sample was scanned asif it were one of the honey samples, but it only contained the solvent(water) and dye. The data were also pre-processed, as stated, so thepeak was set to 10,000.

Data Analysis

MATLAB® (Version 7.8.0.347, Mathworks, USA) and the associatedPLS_Toolbox (Version 6.1, Eigenvector Research Inc., USA) were used toprocess the data and construct predictive models. PLS_Toolbox contains alarge array of multivariate data analysis techniques, with the principalones involving the method of partial least squares (PLS) analysis orN-way PLS (termed NPLS). As the PLS_Toolbox runs under the MATLABprogramming environment, a general purpose analysis Graphical UserInterface (GUI) was used for all of the modelling, and general MATLABfunctions were only used to import and prepare the data prior to loadinginto the Analysis GUI.

NPLS handles and processes data that are presented in two or morespectral dimensions. This makes NPLS a highly appropriate analysis toolas the EEM data consist of two spectral dimensions—the excitation andemission wavelengths.

To judge the performance of a model, it must be applied to evaluate newsamples. Therefore, so that predictions could be made and the modeljustified, some measured samples were deliberately excluded from themodelling exercise and were subsequent re-entered as unknowns toevaluate the predictive model. Unknown fluorescent data can be loadedinto this toolbox as the x data to predict compositional data.

Results Creating the Predictive Model

Using the PLS Toolbox, a model was created based on NPLS analysis. Thismodel was generated using the base set of 75 samples and a set of 114blends of honey. Larger samples make for more accurate predictions soblends were made to increase the number of samples available in thetraining set, as analysis occurs and predictions were made, based ondifferences between fluorescent data.

TABLE 1 A summary of the estimates of compositional data of the honeysamples tested. Samples include honeys 101-135 and 158-175. Samples136-157 lacked the chemical compositional data required to estimatefloral origin. Sample Number Type Manuka (%) Kanuka (%) Pasture (%) 101Manuka 75 0 25 102 Manuka 85 0 15 103 Manuka 62 0 38 104 Manuka 32 0 68105 Manuka 61 0 39 106 Manuka 92 0 8 107 Manuka 32 0 68 108 Manuka 99 01 109 Manuka 80 0 20 110 Manuka 61 0 39 111 Manuka 56 2 42 112 Manuka 952 3 113 Manuka 57 5 38 114 Manuka 66 2 32 115 Manuka 75 2 23 116 Manuka91 0 9 117 Manuka 59 2 39 118 Manuka 78 0 22 119 Manuka 93 0 7 120Manuka 75 0 25 121 Manuka 73 0 27 122 Kanuka 5 70 25 123 Kanuka 5 30 65124 Kanuka 5 25 70 125 Kanuka 5 30 65 126 Kanuka 5 90 5 127 Kanuka 10 855 128 Kanuka 5 90 5 129 Kanuka 10 85 5 130 Kanuka 5 80 15 131 Kanuka 1085 5 132 Kanuka 5 50 45 133 Kanuka 20 20 60 134 Kanuka 10 50 40 135Kanuka 10 40 50 158 Manuka 10 24 66 159 Manuka 20 21 59 160 Manuka 21 2257 161 Manuka 14 34 53 162 Manuka 14 29 57 163 Manuka 16 33 51 164Manuka 25 48 27 165 Manuka 26 53 22 166 Manuka 19 41 40 167 Manuka 18 4339 168 Manuka 39 25 36 169 Manuka 26 11 63 170 Manuka 32 14 54 171Manuka 37 15 48 172 Manuka 50 12 39 173 Manuka 58 5 37 174 Manuka 78 220 175 Manuka 80 5 15

This model used four Latent Variables (LV) that accounted for more than99% of the fluorescent data. An LV is a variable that is inferred usingmathematical models to reduce the dimensionality of the data. The honeysamples had known estimates of the percentage composition (manuka,kanuka and pasture) of some of the honeys based on prior knowledge ofhoney composition (Table 1). This was compared to the fluorescent data.The PLS_Toolbox predicts sample composition based on differencescalculated from the fluorescent data. Therefore, the Toolbox was able tocompare the estimated values with its predictions. The predictions werethen loaded back into the model as the estimated compositional datauntil the model accounted for more than 99% of the variance of thecomposition data, as well as fluorescent data (Table 2).

Some of the honey samples used omitted phenolic composition data sorelative floral (nectar) composition could not be estimated. However, ageneral description of their floral origin was supplied, and so thesehoneys were initially entered as being 100% from that source. Thepredictions made by the Toolbox when compared with the measured valueswere again reloaded into the model until more than 99% of thecompositional data were accounted for.

To extend the library of honey samples, diluted blends of the originalhoneys were scanned and the EEM were generated. The composition datawere calculated from the relative proportions of values from each of themixed honeys. This provided a model that accounted for more than 99% ofthe fluorescent data and 97% of the composition data. As the model makespredictions based on differences in the spectra, a change in thecompositional data entered will change the predictions slightly, witheach cycle giving a more accurate prediction. Therefore, the predictionswere reloaded as the measured compositional data so that the modelaccounted for more than 99% of the fluorescent and composition data(Table 3).

TABLE 2 Predicted floral composition of honey samples used. Samplesinclude honeys 101-188. Sample Number Type Manuka (%) Kanuka (%) Pasture(%) 101 Manuka 51.55 6.71 41.72 102 Manuka 58.16 5.03 36.81 103 Manuka53.79 6.14 40.05 104 Manuka 24.59 3.73 71.69 105 Manuka 49.40 7.25 43.34106 Manuka 63.20 −6.43 43.29 107 Manuka 30.29 3.22 66.49 108 Manuka122.72 5.73 −28.54 109 Manuka 92.45 10.82 −3.36 110 Manuka 78.06 8.0213.85 111 Manuka 40.43 3.34 56.24 112 Manuka 80.29 −5.62 25.33 113Manuka 44.35 6.25 49.41 114 Manuka 70.06 −2.75 32.69 115 Manuka 82.332.48 15.16 116 Manuka 63.70 17.08 19.03 117 Manuka 38.66 21.92 39.29 118Manuka 60.10 18.47 21.25 119 Manuka 32.92 9.97 57.08 120 Manuka 27.877.33 64.78 121 Manuka 33.01 7.11 59.87 122 Kanuka 16.16 25.50 58.32 123Kanuka 6.50 29.98 63.47 124 Kanuka 9.96 28.43 61.59 125 Kanuka 8.3228.59 63.05 126 Kanuka −4.70 101.71 2.99 127 Kanuka 4.47 100.99 −5.43128 Kanuka 22.13 69.36 8.54 129 Kanuka −4.08 108.50 −4.40 130 Kanuka−8.16 111.49 −3.32 131 Kanuka −2.40 115.06 −12.63 132 Kanuka −7.27 59.3447.89 133 Kanuka 26.28 38.83 34.90 134 Kanuka 10.81 30.24 58.94 135Kanuka 3.28 40.70 56.00 136 Kamahi 10.78 10.77 78.45 137 Kamahi 12.108.30 79.59 138 Towai 7.25 20.24 72.48 139 Rata 9.53 9.41 81.05 140Pohutukawa 8.74 8.39 82.85 141 Clover 6.87 16.79 76.31 142 Clover 9.7111.09 79.19 143 Tawari 6.93 9.47 83.58 144 Rewarewa 13.67 5.62 80.71 145Rewarewa 18.71 14.14 67.14 146 Rewarewa 18.97 6.31 74.72 147 Honeydew28.97 10.14 60.93 148 Honeydew 21.27 11.67 67.08 149 Kanuka 17.77 64.6717.48 150 Kanuka 14.27 47.28 38.40 151 Kanuka 2.16 88.97 8.84 152 Kanuka8.48 77.74 13.74 153 Kanuka 7.81 64.18 27.95 154 Kanuka 2.88 83.65 13.45155 Kanuka 8.84 57.38 33.74 156 Kanuka 12.87 73.05 14.08 157 Kanuka 4.7285.36 9.94 158 Manuka 23.39 21.63 54.96 159 Manuka 24.71 20.11 55.18 160Manuka 26.46 16.77 56.76 161 Manuka 24.26 41.40 34.31 162 Manuka 25.4933.33 41.16 163 Manuka 28.58 31.84 39.57 164 Manuka 15.72 45.67 38.59165 Manuka 19.04 43.86 37.08 166 Manuka 22.83 35.69 41.46 167 Manuka19.27 39.18 41.54 168 Manuka 52.26 16.42 31.34 169 Manuka 42.31 8.7548.97 170 Manuka 51.73 4.55 43.73 171 Manuka 45.21 10.49 44.29 172Manuka 49.46 5.62 44.93 173 Manuka 61.04 2.64 36.37 174 Manuka 66.60−6.19 39.65 175 Manuka 61.33 −2.06 40.78 176 Unknown 19.15 21.02 59.85177 Unknown 11.92 45.97 42.11 178 Unknown 56/06 4.52 39.48 179 Unknown54.80 7.16 38.10 180 Unknown 4.39 76.15 19.45 181 Unknown 40.76 14.1045.13 182 Unknown 15.64 25.84 58.50 183 Unknown 10.28 28.01 61.70 184Unknown 4.20 95.56 0.27 185 Unknown 10.08 29.07 60.84 186 Unknown 16.077.71 76.22 187 Unknown 6.36 18.32 75.30 188 Unknown 10.26 12.25 77.48

TABLE 3 Predicted floral composition of artificially blended honeys.Samples assigned numbers 201-314. Sample Original Original Manuka KanukaPasture number Sample 1 Sample 2 (%) (%) (%) 201 101 139 27.81 9.4762.70 202 102 140 29.60 10.16 60.24 203 103 141 26.63 14.09 59.26 204104 142 16.92 11.29 71.78 205 105 143 25.19 11.12 63.67 206 106 14439.72 2.21 58.11 207 107 145 24.58 12.01 63.41 208 108 146 60.25 9.3430.36 209 109 148 55.21 16.71 28.04 210 110 147 54.41 9.24 36.36 211 111149 23.47 43.05 33.45 212 112 150 47.63 21.37 31.00 213 113 151 20.3347.06 32.59 214 114 152 35.50 31.65 32.84 215 115 153 37.69 33.74 28.54216 116 154 33.16 46.62 20.17 217 117 155 20.10 42.89 36.95 218 118 15633.72 47.68 18.55 219 119 157 15.60 56.07 28.32 220 120 158 24.54 14.9860.47 221 121 159 26.01 15.28 58.70 222 122 160 20.23 19.96 59.80 223123 161 14.36 31.51 54.10 224 124 162 17.04 25.22 57.73 225 125 16315.90 26.05 58.03 226 126 164 5.60 73.44 20.97 227 127 165 7.14 76.3416.53 228 128 166 18.58 50.94 30.50 229 129 167 5.27 75.01 19.73 230 130168 21.18 56.10 22.73 231 131 169 11.35 74.24 14.43 232 132 170 20.0429.82 50.13 233 133 171 29.53 24.70 45.77 234 134 172 29.62 17.13 53.26235 135 173 27.35 22.62 50.04 236 136 174 38.88 0.41 60.74 237 137 17532.78 5.96 61.28 238 138 101 28.31 12.30 59.38 239 159 101 37.15 13.7649.08 240 160 102 46.83 11.72 41.45 241 161 103 37.49 20.05 42.44 242162 104 24.20 21.87 53.94 243 163 105 35.97 17.49 46.52 244 164 10643.68 20.88 35.48 245 165 107 22.16 25.04 52.79 246 166 108 71.75 19.648.60 247 167 109 53.14 26.44 20.37 248 168 110 68.80 12.69 18.52 249 169111 37.11 6.63 56.27 250 170 112 67.67 4.10 28.26 251 171 113 41.5610.08 48.36 252 172 114 60.95 4.67 34.40 253 173 115 65.42 1.82 32.79254 174 116 69.15 5.10 25.73 255 175 117 49.30 7.67 43.01 256 129 11831.41 51.83 16.73 257 130 119 11.30 56.63 32.07 258 131 120 11.18 63.8425.00 259 132 121 13.31 31.45 55.23 260 133 122 21.60 28.78 49.62 261134 123 9.77 26.72 63.49 262 135 124 7.91 32.92 59.15 263 136 125 10.2416.21 73.53 264 137 126 2.76 56.44 40.81 265 138 127 13.43 60.53 26.08266 139 128 12.73 42.20 45.08 267 140 151 5.90 41.95 52.13 268 141 1526.45 44.39 49.14 269 142 153 8.32 35.49 56.16 270 143 154 1.97 53.5344.48 271 144 155 11.23 33.08 55.67 272 145 156 16.00 46.40 37.61 273146 157 10.69 48.34 40.97 274 147 158 27.90 14.59 57.53 275 148 15020.04 30.25 49.71 276 149 101 36.36 29.59 34.03 277 101 102 58.14 5.9235.94 278 103 104 39.91 6.84 53.26 279 105 106 59.78 1.95 38.29 280 107108 69.98 5.23 24.77 281 109 110 98.50 13.67 −12.27 282 111 112 62.470.56 36.98 283 113 114 56.84 6.68 36.46 284 115 116 75.14 5.04 19.77 285117 118 50.24 19.69 29.93 286 119 120 29.34 10.10 60.55 287 121 12224.01 18.19 57.78 288 123 124 9.84 28.05 62.09 289 125 126 −0.97 69.0931.87 290 127 128 13.87 73.66 12.51 291 129 130 −5.21 100.19 5.03 292131 132 −1.89 81.27 20.62 293 133 134 16.42 34.13 49.45 294 135 136 7.7124.94 67.34 295 137 138 10.52 16.00 73.47 296 139 140 9.23 10.84 79.91297 141 142 8.20 16.24 75.53 298 143 144 11.45 8.53 80.02 299 145 14620.78 11.44 67.78 300 147 148 27.71 10.89 61.44 301 149 150 13.63 60.5425.78 302 151 152 7.61 71.25 21.12 303 153 154 2.55 76.87 20.56 304 155156 12.17 71.50 16.34 305 157 158 14.90 52.98 32.12 306 159 160 23.6617.45 58.89 307 161 162 20.91 34.58 44.50 308 163 164 23.44 27.93 48.61309 165 166 19.55 36.75 43.69 310 167 168 33.30 25.23 41.48 311 169 17039.92 8.14 51.95 312 171 172 45.33 8.67 46.00 313 173 174 57.40 0.2942.36 314 175 174 60.83 −3.38 42.60

The model building process was iterative, that is, predictions from theoutcome of a round of modelling were fed back into the model as originalestimates, and predictions recalculated. This was done to refine theoriginal estimates, which were based on intuitive‘off-the-top-of-the-head’ assessments using constituent compositionalanalyses (abundance of certain constituent markers correlating to honeysand nectars of known floral origin. The iterative refinement processreduces model variance, or ‘trains the model’, by adjusting the floralcompositional weightings. This process is similar to the supervisedlearning refinement of neural network models, particularly associatedwith pattern recognition with systems containing high levels of noise orvariation. This process ultimately results in a library or ‘trainingset’ of known honeys, generated from a large set of samples with avariety of floral origins, to be used to generate predictions oncompositions of unknown samples from their respective fluorescent EEMdata. Note that this iteratively trained data set is the basis for allfurther comparisons described in this report.

Model Validation

First, to validate the model, a sample or set of samples were used witha known composition. A working model should accurately predict thecomposition. Therefore, manual tests were conducted using a form ofindependent sample validation: every tenth sample (samples 110, 120,130, etc.) were excluded from the model and the model remade with allhoneys except these excluded samples. The excluded samples were then fedback in to the model as unknowns to test whether they yielded the samepredicted outcomes as when they were originally part of the model. Thiscomparison was made by comparing predicted values for those samplesagainst the predicted floral composition from the model made with allhoneys. This revealed that the prediction was accurate to a mean squareresidual error (rmse) of 2.72% (FIG. 1). The maximum difference betweentrained and predicted outcomes was 8.09% (Table 4).

Predicting Floral Origin of Unknown Samples

After validating the model, 13 truly unknown samples (for which nocompositional or floral data was known) were loaded into the model andpredictions were made without any original knowledge of the composition.The results predicted by the full scan model are displayed in Table 5.

TABLE 4 Validation of the accuracy of the model Trained data compositionValidation data Differences Sample Manuka Kanuka Pasture Manuka KanukaPasture Manuka Kanuka Pasture number (%) (%) (%) (%) (%) (%) (%) (%) (%)110 78.06 8.02 13.85 77.95 8.00 13.97 0.10 0.02 −0.13 120 27.87 7.3364.78 27.76 7.25 64.97 0.11 0.08 −0.19 130 −8.16 111.49 −3.32 −7.97111.57 −3.60 −0.20 −0.09 0.28 140 8.74 8.39 82.85 8.52 8.22 83.25 0.220.17 −0.39 150 14.27 47.28 38.40 13.99 47.05 38.91 0.28 0.24 −0.51 16026.46 16.77 56.76 26.40 16.74 56.85 0.06 0.03 −0.09 170 51.73 4.55 43.7351.78 4.62 43.62 −0.05 −0.07 0.11 180 28.01 9.13 62.85 25.19 11.10 63.692.82 −1.98 −0.84 190 44.89 34.05 21.01 37.62 33.65 28.70 7.27 0.41 −7.69200 18.46 29.67 51.84 16.05 26.18 57.75 2.41 3.49 −5.91 210 32.07 22.0845.87 27.38 22.62 50.01 4.69 −0.54 −4.14 220 24.47 24.37 51.15 22.0824.97 52.94 2.39 −0.60 −1.79 230 50.31 10.68 38.97 49.27 7.55 43.15 1.033.13 −4.18 240 5.33 61.08 33.60 13.41 60.46 26.16 −8.09 0.62 7.44 25018.45 31.25 50.29 19.96 30.17 49.87 −1.51 1.09 0.41 260 49.67 20.0730.10 50.40 19.73 29.73 −0.73 0.34 0.37 270 9.69 15.20 75.10 10.50 15.9873.51 −0.81 −0.78 1.59 280 14.83 53.59 31.57 14.83 52.94 32.23 −0.010.66 −0.66

TABLE 5 Predictions of unknown honey sample compositions using a fullscan and three excitation wavelengths. Predictions using Predictionsusing 3 a full scan excitation wavelengths Differences Sample ManukaKanuka Pasture Manuka Kanuka Pasture Manuka Kanuka Pasture number (%)(%) (%) (%) (%) (%) (%) (%) (%) 1 19.15 21.02 59.85 19.18 18.44 62.4−0.03 2.58 −2.54 2 11.92 45.97 42.11 10.98 46.89 42.13 0.95 −0.92 −0.023 56.06 4.52 39.48 55.24 3.53 41.27 0.81 0.99 −1.79 4 54.80 7.16 38.1054.38 7.46 38.2 0.42 −0.31 −0.10 5 4.39 76.15 19.45 5.19 73.54 21.24−0.80 2.61 −1.80 6 40.76 14.10 45.13 41.06 14.23 44.7 −0.30 −0.13 0.43 715.64 25.84 58.50 17.26 24.02 58.69 −1.62 1.82 −0.19 8 10.28 28.01 61.7010.23 23.98 65.76 0.05 4.03 −4.07 9 4.20 95.56 0.27 6.01 90.25 3.76−1.81 5.31 −3.49 10 10.08 29.07 60.84 11.19 28.23 60.57 −1.11 0.84 0.2711 16.07 7.71 76.22 17.79 7.96 74.26 −1.72 −0.25 1.96 12 6.36 18.3275.30 6.66 16.18 77.13 −0.31 2.14 −1.83 13 10.26 12.25 77.48 12.35 12.1575.5 −2.09 0.10 1.99

Example 2

The method was tested to determine whether restricting the analysis todiscrete fluorescent peaks (‘peak picking’) yielded predictions withsimilar accuracy to the use of the full EEM. Furthermore, if three orfour excitation wavelengths could be used instead of full EEM scanningthen it would be simpler and cheaper to build a portable instrument foruse in the field.

Therefore, the wavelengths chosen were 230, 265 and 335 nm and theiremission scans recorded. These excitation wavelengths were chosen asthey were the co-ordinates of the main peaks found in manuka and kanukahoneys. Reducing the number of co-ordinates from the original EEM tojust three excitation wavelengths resulted in predicted floral originsvarying from EEM-based predictions by a mean square residual value of1.88% (FIG. 2), with the largest individual sample variance being 5.31%(Table 5—see Example 1).

Example 3

The floral composition of a honey may be estimated using the phenolicprofile of the honey. The concentrations of the subset of the phenolicand antimicrobial compounds that yield the greatest floraldiscrimination were identified from the original set of samples used inExample 1.

The NPLS model described in Example 1 was used to correlate EEM profileswith the chemical data of the known honey samples. Of the 75 honeysamples, 66 of the honey samples had full discriminatory chemicalprofiles, with the other 9 honeys lacked the antimicrobial methylglyoxal(MGO) and 4-methoxyphenyllactic acid concentrations. Available chemicaldata was entered into the model instead of the floral composition datato determine if these chemical data matched the fluorescent EEMprofiles. In addition to correlating and comparing individual chemicalconstituents, the ratio of dihydroxy acetone (DHA, a precursor to MGO)to MGO (which corresponds to the age and maturity of manuka honey andthe sum of MGO and its DHA precursor were also compared. MGO is themolecule responsible for the unique antimicrobial activity of the manukahoney, from which the UMF value is derived. It is the UMF value whichpredominantly determines the monetary value of manuka honey.

There was a correlation (R2 above 0.854 using 4 LV) between thefluorescent data and the MGO values, dihydroxy acetone (DHA), and methylsyringate. When the measured concentrations of constituents were plottedagainst the predictions made by the Toolbox, more than 85% of theconcentrations for these constituents was accounted for using NPLS modeland fluorescent data (FIGS. 3 to 10).

The correlation between the fluorescence profile and some of thephenolic constituents of the honey is not surprising. However, thecorrelation between the phenolic profiles and levels of theantimicrobial (UMF®) molecule MGO and its precursor DHA was not entirelyexpected.

Example 4

In this example, a practical demonstration of honey fluorescenceprofiling is illustrated.

After the model was created as described in Example 1, the model wastested to ensure that the model could identify mislabelled honeys.

A batch of 10 samples (and some constituent concentration data) weretested having been sourced from commercial honey products on sale in theIndonesian market. These samples were suspected of stating a higher UMFvalue than the honey actually possessed or the botanical origin wassuspected to not be as per label.

Method

The unknown samples were scanned following the methods outlined inExample 1. The EEMs were then loaded into Matlab and compared with modelpredictions.

Results

The fluorescence analysis can identify honeys that have beenmislabelled. Data in Table 6 present the model's predictions of honeycomposition, according to their phenolic profiles. The predictions madeare similar to the predictions made based on known honey sample phenolicprofiles.

TABLE 6 Predictions of compositional data of honey samples bought inIndonesia Honey Sample (Manufac- 2 4 Methoxy Suspected turer MethoxyPhenyl- Phenyl- UMF by description names Lable Methyl Benzoic lacticlactic Dihydroxy Methyl- MGO of % % % removed) Claim Syringate acid acidacid Acetone glyoxal calculator HMF honey Manuka Kanuka Pasture 1 25+271 1.1 1230 65.2 175 143 6.7 36 Aged −1.61 85.99 15.62 kanuka, somemanuka content 2 18+ 136 1.5 470 50.8 0 8 na 450 Heated 24.03 46.0729.97 kanuka/ pasture blend 3 20+ 109 5.7 943 16.3 124 402 12.6 156Suspect 63.93 0.78 35.38 manuka/ kanuka/ pasture 4  8+ 79.3 3.6 684 14.1418 320 10.8 17 Manuka/ 33.75 11.83 54.44 kanuka/ pasture blend 5 25+17.1 143 1600 9.1 1310 1219 26.5 34 Suspect 51.38 2.80 45.86 manuka 620+ 293 63.2 608 5.7 1030 1054 24.2 83 Suspect 87.98 −3.28 15.23 manuka/pasture 7 30+ 41.1 2.8 501 5.2 445 281 9.9 24 Manuka/ 30.93 10.38 58.69pasture blend 8 30+ 27.3 3 595 3.5 629 290 10.1 31 Manuka/ 40.30 3.5956.13 pasture blend 9 15+ 176 1.8 608 63.8 106 61 4.7 12 Kanuka/ 2.6386.08 11.29 pasture blend, some manuka content 10 25+ 30.4 115 1410 8.4328 863 21.3 200 Suspect 57.05 −4.65 47.67 manuka

In conclusion, the floral composition of unknown honeys could bepredicted. These predictions matched the phenolic profiles of thehoneys. Their phenolic profiles determined that the honeys did not matchtheir labelled origins. Further the analysis showed that the stated UMFactivity was not always true to label. Further HMF levels detected usingthe method showed that some of the honeys had been heated. Therefore,fluorescence-based analysis allows rapid determination of mislabelledhoneys.

Example 5

The effect of pH on the fluorescence of the honey samples wasinvestigated. The optimal range of the Dylight dyes and AF 594 is frompH 4 to 9. Previous studies on honeys have shown that the pH of thehoney used in previous trials ranged from pH 3 to 5.

The pH of the honeys tested in earlier examples for fluorescence wastested to determine if the pH was within the optimal range of the dye.This would ensure that an accurate reading of the dye was taken. The pHof all the honey samples in this set was measured in the dilutedconcentration (2% w/v). This showed that the samples analysed in thisset all ranged from pH 3.8 to 5.6 with only two samples having a pHunder 4.0. Therefore, no modification of the pH was deemed necessary.

Example 6

It is understood by the applicants that inconsistent intensity levelsseen for the peak located at 590 nm excitation and 620 nm emission maybe due to fluorescence quenching.

Fluorescence quenching involves a decrease in fluorescence intensity.Quenching is heavily dependent on pressure, temperature and can be aresult of many processes such as excited state reactions, energytransfer, collisional quenching and complex formation. Molecular O₂ andiodine are common chemical quenchers. A probable cause of quenching isdue to Dexter (collisional energy transfer). This could be due to thedye and the honey molecules colliding so that non-fluorescent compoundsabsorb the energy from excited state molecules and it is instead used invibrational movement or heat. This is a phenomenon that is short rangedand dependent on the overlap of the molecular orbitals.

The presence of the honey in a solution may change the hydrophobicity ofthe dye. If the dye is more hydrophobic it is more likely to stacktogether and exclude the water molecules. This could also change thefluorescent properties.

In previous experiments all the samples were run at 2% (w/v) honey and0.05% (v/v) Alexa Fluor 594. A lower concentration might decrease theeffects of quenching as there would be a larger spacing betweenmolecules, so less orbital overlap between molecules.

The aim of this experiment was to determine if using a lowerconcentration of honey and dye would reduce the effects of quenching.

Method

A honey sample (kanuka honey, sample number 122 above) was randomlychosen. A set of subsamples was made using varying concentrations ofdye, honey and different machine settings (Table A).

TABLE 9 Composition of subsamples and instrument settings. Sub-sampleConcentration of Concentration of number honey (% (w/v)) dye (% (v/v))Gain 1 2 0.05 85 2 1 0.025 90 3 0.6666 0.01666 100 4 0.5 0.0125 100 50.3333 0.0083 100 6 0.2 0.005 100 7 0.1 0.0025 130 8 10 0.25 85 9 50.125 85 10 3.333 0.0833 85 11 2.5 0.0625 85 12 0.25 0.00625 90 13 0.20.005 110 14 0.3333 0.0083 110 15 0.1666 0.00416 110 16 0.125 0.003125110

Results

The ratio of the two peaks from the dye was used to analyse the effectof quenching. When there is a lower concentration of honey in thesolution the ratio is decreased (FIG. 11). There is an apparentlylogarithmic relationship between ratio of the dye peaks and theconcentration of the dye and honey.

When the concentration of honey is less than 0.33% (w/v) the shape ofthe honey spectra changes. Below this level, the peak at 265 excitation,275 emission—an important co-ordinate required for analysis anddifferentiation of honeys—no longer displayed a shoulder.

A strong quenching effect is seen at high concentrations of honey. Atconcentrations above 2% (w/v), honey peaks display similar-shapedprofiles to those seen in previous experiments that used 2% (w/v).

Therefore, as there was no more detail seen at higher concentrations ofhoney, the maximum concentration of honey was considered to be 2%.However, when the honey concentration is decreased, the ratio betweenthe two heights of the honey peaks (230,310 and 265,375) also starts tochange (FIG. 12).

Conclusion

A 0.2% to 5% concentration may be used, more specifically 1.5 to 2.5%,and optionally 2% (w/v) honey solution may be used, provided the correctbalance between minimising the affects of quenching but also retainingimportant coordinates of the honey fluorescence, and would continue tobe used in the analysis of honey samples by fluorescence, with a 0.05%Alexa Fluor Dye 594 solution.

Example 7

Photo bleaching of honey samples was investigated. As noted above, theAlexa Dye 594 is not photo stable, as was shown by a 30% decrease inintensity after 24 h of exposure to sunlight in our trials completed bythe applicants.

Method

Two randomly selected honey samples were chosen, a manuka (102) and akanuka (126). Each honey was analysed in diluted and undiluted form,after either being stored for 4 h in the dark or 4 h under lights withan intensity of 650 μmol. The diluted samples were diluted in deionisedwater to 2% (w/v) samples and stored in a volume of 0.5 mL forconsistency. Duplicates of each samples were recorded and the averagetaken.

Results

When the diluted kanuka was exposed to light, the overall intensitylevel almost halved (FIG. 13). When bleached in the light, there is achange in shape of the profile of the kanuka honey from the unbleachedhoney. Peaks emerge at 250,380 250,440 and 250,500. Closer inspection ofthe unbleached spectrum showed that in the other spectra, these areobserved as peak shoulders—peaks that are obscured from view by largerpeaks with similar co-ordinates. Therefore, the compound that fluorescesin this region is not subject to being bleached by the light.

No photo bleaching occurred in the diluted manuka honey or theconcentrated samples (FIGS. 14-16). These had consistent intensitylevels and profile shapes.

Conclusions

It is proposed by the applicants that, as long as the diluted samplesare not exposed to sunlight for periods of time exceeding thosepresented here, photo bleaching is not considered a significant problem.

Example 8

Honey is concentrated nectar. Therefore, many of the phenolicconstituents of honey are present in nectar. Previous work found thatthe nectar samples had similar fluorescent profiles to older, moremature honeys. This experiment aimed to determine if nectar samples of100% manuka origin generated a similar profile to that of a 100% manukahoney sample.

Methods

Thirteen nectar samples supplied were diluted to 4% (w/v) solution,diluted in a 0.05% (v/v) solution of Alexa Fluor 594 dye. These werescanned as described in earlier examples.

Results

Varying peak shapes and locations were seen in the fluorescent spectrumrecorded from nectar samples.

The fluorescence spectrum of the wild nectars (FIG. 17) showed thatSites A and B yielded slightly non-characteristic profiles whereas SitesC, D and E had profile shapes characteristic of manuka honeys.

The fluorescence spectra that were obtained from nectar samples thatwere collected in a glasshouse had a variety of shapes (FIG. 18). SampleA had a strong peak at 245,385, which had not been associated withmanuka honeys before. This could be due to contamination or somethingthat needs to be further explored. Samples C and E are cultivars of thesame species as Sample A. These two spectra had very similar shapes andintensity levels but were not the same as Sample A.

When the nectar samples were compared against the honey samples in themodel made in Section 3, the nectar samples were identified as outliersin the hotelling T₂ vs. Q residuals plot (FIG. 19). The intensity levelsof the fluorescence spectra cannot be compared to that of the honeysamples as the concentrations of these samples differed and, therefore,the nectar samples were outliers when the models were compared.

Nectar samples had fluorescence EEM determined with a 4% (w/v) solution.

Conclusion

In most cases, the fluorescent profiles resembled those of theircorresponding honeys and it was the applicants finding that bystandardising concentrations or developing a model that standardisesfluorescent intensities, any further variations could be overcome.

Aspects of the methods and device have been described by way of exampleonly and it should be appreciated that modifications and additions maybe made thereto without departing from the scope of the claims herein.

1. A method for determining the concentration values of key constituentchemicals of honey, comprising the steps of: (a) estimating thebotanical origin of at least one standard honey sample, by: (i)obtaining key constituent chemical concentrations; and (ii) assigningthe botanical origin as a numerical value on the basis of abundance ofchemical compounds characteristic of certain botanical groups; (b)generating the fluorescence signature of standard honey samples, by: (i)exciting a diluted honey sample solution with light of wavelengths overthe range 200-700 nm at increasing wavelength increments; and (ii)measuring the intensity of the fluorescent light emitted from theexcited solution over the range 280-650 nm at increasing wavelengthincrements; and (iii) measuring the wavelength of the fluorescent lightemitted from the excited solution over the range 280-650 nm atincreasing wavelength increments; and (iv) combining excitation andemitted light as 2-dimensional excitation-emission matrix (EEM); (c)constructing a validated predictive mathematical model from standardhoney data, by: (i) using the botanical origin value determined in step(a) as the first matrix in a multivariate analysis; (ii) using thefluorescence EEM data determined in step (b) as the second matrix in amultivariate analysis; (iii) generating a mathematical model using thesetwo matrices; and, (iv) establishing a statistical confidence ofpredictive power of mathematical model with leave-one-out validationprocess; (d) generating the fluorescence EEM signature of an unknownhoney sample or samples, as outlined in step (b); and (e) using theunknown honey fluorescence EEM data from step (d) with the validatedmathematical model of step (c) to predict and assign concentrationvalues of key constituent chemicals of honey with defined statisticalconfidence.
 2. A method for determining the botanical origin of honeyincluding the steps of: (a) estimating the botanical origin of standardhoney samples, by: (i) obtaining key constituent chemicalconcentrations; and (ii) assigning the botanical origin as a numericalvalue on the basis of abundance of chemical compounds characteristic ofcertain botanical groups; (b) generating the fluorescence signature ofstandard honey samples, by: (i) exciting a diluted honey sample solutionwith light of wavelengths over the range 200-700 nm at increasingwavelength increments; and (ii) measuring the intensity of thefluorescent light emitted from the excited solution over the range280-650 nm at increasing wavelength increments; and (iii) measuring thewavelength of the fluorescent light emitted from the excited solutionover the range 280-650 nm at increasing wavelength increments; and (iv)combining excitation and emitted light as 2-dimensionalexcitation-emission matrix (EEM); (c) constructing a validatedpredictive mathematical model from standard honey data, by: (i) usingthe botanical origin value determined in step (a) as the first matrix ina multivariate analysis; (ii) using the fluorescence EEM data determinedin step (b) as the second matrix in a multivariate analysis; (iii)generating a mathematical model using these two matrices; and, (iv)establishing a statistical confidence of predictive power ofmathematical model with leave-one-out validation process; (d) generatingthe fluorescence EEM signature of an unknown honey sample or samples, asoutlined in step (b); and (e) using the unknown honey fluorescence EEMdata from step (d) with the validated mathematical model of step (c) topredict and assign numerical value of botanical origin of honey withdefined statistical confidence.
 3. The method as claimed in claim 2wherein the numerical value of botanical origin is expressed as apercentage manuka honey, percentage kanuka honey, percentage otherspecific floral origin honey, percentage other origin honey as a sum, orcombinations thereof.
 4. The method as claimed in claim 1 wherein theconstituent chemicals in a honey and/or the honey floral origin aredetermined instead by analysis of the nectar from which the honey isderived.
 5. The method as claimed in claim 1 wherein the fluorescencesignature is generated using excitation wavelengths in the range 200-700nm.
 6. The method as claimed in claim 1 wherein the fluorescencesignature is generated using the key excitation wavelengths, 230 nm, 265nm, and 335 nm.
 7. The method as claimed in claim 1 wherein theleave-one-out validation process uses a partial least squares (PLS)analysis.
 8. The method as claimed in claim 1 wherein the keyconstituent chemical include compounds selected from the groupconsisting of: methyl syringate, 2-methoxybenzoic acid, phenyllacticacid, 4-methoxyphenyllactic acid, dihydroxyacetone, methylglyoxal, andcombinations thereof.
 9. The method as claimed in claim 1 wherein thesample or samples are initially diluted to a 0.2 to 5% w/v solutionusing water.
 10. The method as claimed in claim 1 wherein the method isused to identify the concentration of manuka, kanuka and other floralorigin honey in a honey sample.
 11. The method as claimed in claim 1wherein the method is used to determine the UMF activity of a honeysample.
 12. A device for identifying honey botanical origin and/orchemical constituents that includes a sample receiving area into which ahoney sample is inserted and the device subsequently identifies thehoney botanical origin and chemical constituents via a method comprisingthe steps of: (a) estimating the botanical origin of at least onestandard honey sample, by: (i) obtaining key constituent chemicalconcentrations; and (ii) assigning the botanical origin as a numericalvalue on the basis of abundance of chemical compounds characteristic ofcertain botanical groups; (b) generating the fluorescence signature ofstandard honey samples, by: (i) exciting a diluted honey sample solutionwith light of wavelengths over the range 200-700 nm at increasingwavelength increments; and (ii) measuring the intensity of thefluorescent light emitted from the excited solution over the range280-650 nm at increasing wavelength increments; and (iii) measuring thewavelength of the fluorescent light emitted from the excited solutionover the range 280-650 nm at increasing wavelength increments; and (iv)combining excitation and emitted light as 2-dimensionalexcitation-emission matrix (EEM); (c) constructing a validatedpredictive mathematical model from standard honey data, by: (i) usingthe botanical origin value determined in step (a) as the first matrix ina multivariate analysis; (ii) using the fluorescence EEM data determinedin step (b) as the second matrix in a multivariate analysis; (iii)generating a mathematical model using these two matrices; and, (iv)establishing a statistical confidence of predictive power ofmathematical model with leave-one-out validation process; (d) generatingthe fluorescence EEM signature of an unknown honey sample or samples, asoutlined in step (b); and (e) using the unknown honey fluorescence EEMdata from step (d) with the validated mathematical model of step (c) topredict and assign concentration values of key constituent chemicals ofhoney with defined statistical confidence.